基于冗余伪观测的自适应滤波算法

Pseudo Redundant Measurement-Based Adaptive Kalman Filter

关键词:  自适应滤波  观测噪声方差  冗余测量  伪观测  目标跟踪

Key words:  Adaptive filters  Measurement noise covariance  Redundant measurement  Pseudo-measurement  Target tracking

摘要:

现有的二阶互差分(SOMD)算法能够给出与状态估计误差解耦的观测噪声协方差估计，但是需要满足冗余测量的条件，但这一条件往往难以满足。 针对这一问题，提出了一种利用状态预测值构造相邻2个时刻伪观测的方法，将原SOMD算法扩展到具有单测量的系统中。使用目标跟踪问题对该算法的有效性进行验证。仿真结果表明，当采样周期较小时，该算法能够忽略状态估计误差的影响并给出较准确的观测噪声方差，在精度和鲁棒性方面优于其他参考算法。

Abstract:

Existing second-order mutual difference (SOMD) algorithm can give the estimation of measurement noise covariance decoupled with the state estimation error, but it requires redundant measurements which are generally not satisfied. This paper proposes a method for constructing pseudo-measurement of two adjacent moments using state prediction, and then the SOMD algorithm is expanded to the system with a single measurement. The efficiency of the approach is verified via a target tracking problem. Simulation results indicate that the proposed algorithm can ignore the influence of state estimation error when the sampling period is small and provide accurate measurement noise properties, which is superior to other reference algorithms in accuracy and robustness.